class_docstr: Implement ResNet backbone for text recognition, modified from `ResNet.
  
  <https://arxiv.org/pdf/1512.03385.pdf>`_ and
  `<https://github.com/FangShancheng/ABINet>`_
  
  Args:
      in_channels (int): Number of channels of input image tensor.
      stem_channels (int): Number of stem channels.
      base_channels (int): Number of base channels.
      arch_settings  (list[int]): List of BasicBlock number for each stage.
      strides (Sequence[int]): Strides of the first block of each stage.
      out_indices (None | Sequence[int]): Indices of output stages. If not
          specified, only the last stage will be returned.
      last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
code: def _make_stem_layer(self, in_channels, stem_channels):
      self.conv1 = nn.Conv2d(
          in_channels, stem_channels, kernel_size=3, stride=1, padding=1
      )
      self.bn1 = nn.BatchNorm2d(stem_channels)
      self.relu1 = nn.ReLU(inplace=True)
  
file_path: mmocr/mmocr/models/textrecog/backbones/resnet_abi.py
imports: import torch.nn
  from mmengine.model import BaseModule, Sequential
  import mmocr.utils
  from mmocr.models.textrecog.layers import BasicBlock
  from mmocr.registry import MODELS
label_desc: Use image node_id 3 for calling the ResNetABI obj's underlying member method code with example usage: obj._make_stem_layer(in_channels, stem_channels) without return types